Isfahan Artificial Intelligence Event 2023: Reflux Detection Competition - Azra Rasouli Kenari; Ahmadreza Montazerolghaem; Zahra Zojaji; Mehdi Ghatee; Behnam Yousefimehr; Amin Rahmani; Mahdi Kalani; Farnoush Kiyanpour; Mohamad Kiani-Abari; Mohammad Yasin Fakhar; Safiyeh Rezaei; Mojtaba Tahernia; et al. (Journal of Medical Signals and Sensors, 2025)
Background: Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection. Method: In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data. Result: A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations. Discussion: This article outlines the datasets provided to participants and offers an overview of the competition results.